Draft
Final Project
11/17/2018
Importing data
load("./data/Wave 1/DS0001/34315-0001-Data.rda")
load("./data/Wave 2/DS0001/37105-0001-Data.rda")
load("./data/Wave 3/DS0001/37106-0001-Data.rda")
wave_1_data = da34315.0001
wave_2_data = da37105.0001
wave_3_data = da37106.0001Creating Graphs
countplot = wave_3_data %>%
select(PH001, PH002, SEX) %>%
mutate(physical = case_when(
PH001 == "(01) Excellent" ~ "Excellent",
PH001 == "(02) Very Good" ~ "Very Good",
PH001 == "(03) Good" ~ "Good",
PH001 == "(04) Fair" ~ "Fair",
PH001 == "(05) Poor" ~ "Poor"),
mental = case_when(
PH002 == "(01) Excellent" ~ "Excellent",
PH002 == "(02) Very Good" ~ "Very Good",
PH002 == "(03) Good" ~ "Good",
PH002 == "(04) Fair" ~ "Fair",
PH002 == "(05) Poor" ~ "Poor"),
sex = case_when(
SEX == "(1) Male" ~ "Male",
SEX == "(2) Female" ~ "Female"
),
physical = factor(physical, levels = c("Excellent", "Very Good", "Good", "Fair", "Poor")),
mental = factor(mental, levels = c("Excellent", "Very Good", "Good", "Fair", "Poor"))
) %>%
select(-PH001, -PH002, -SEX)
plot_1 =
countplot %>%
ggplot(aes(x = physical, y = mental, color = ..n..)) +
geom_count(alpha = 0.8) +
labs(
x = "Self-Rated Physical Health",
y = "Self-Rated Mental Health"
) +
facet_grid(~sex) +
theme_bw() +
theme(legend.position = "none")
ggplotly(plot_1)wave_1_data = wave_1_data %>%
mutate(MHUCLA_LONELINESS_1 = MHUCLA_LONELINESS)
wave_2_data = wave_2_data %>%
mutate(MHUCLA_LONELINESS_2 = MHUCLA_LONELINESS)
wave_3_data = wave_3_data %>%
mutate(MHUCLA_LONELINESS_3 = MHUCLA_LONELINESS)
spaghetti =
merge(wave_1_data, wave_2_data, by = "ID") %>%
merge(wave_3_data, by = "ID") %>%
select(ID, SEX.x, CS006, MHUCLA_LONELINESS_1, MHUCLA_LONELINESS_2, MHUCLA_LONELINESS_3) %>%
arrange(ID) %>%
mutate(sex = case_when(
SEX.x == "(1) Male" ~ "Male",
SEX.x == "(2) Female" ~ "Female"
),
marital_status = case_when(
CS006 == "(1) Married" ~ "Married",
CS006 == "(2) Living with a partner as if married" ~ "Cohabitation",
CS006 == "(3) Single(never married)" ~ "Single",
CS006 == "(4) Separated" ~ "Separated",
CS006 == "(5) Divorced" ~ "Divorced",
CS006 == "(6) Widowed" ~ "Widowed"
)) %>%
filter(marital_status == "Widowed") %>%
select(-SEX.x, -CS006) %>%
gather(key = wave, value = loneliness_value, MHUCLA_LONELINESS_1:MHUCLA_LONELINESS_3) %>%
mutate(wave = case_when(
wave == "MHUCLA_LONELINESS_1" ~ 1,
wave == "MHUCLA_LONELINESS_2" ~ 2,
wave == "MHUCLA_LONELINESS_3" ~ 3
)) %>%
janitor::clean_names()
spaghetti %>%
filter(sex == "Male", loneliness_value != "NA") %>%
ggplot(aes(x = wave, y = loneliness_value, color = id)) +
geom_line() +
theme_bw() +
theme(legend.position = "none")
# ICD-10: bar graph of proportions of each ICD-10 code to determine which ICD-10 diagnosis accounts for the greatest burden of disease in our population. We can potentially facet by sex to see if the burden is different for males and females.
bar_graph =
wave_3_data %>%
select(ID, ICD10_01:ICD10_16) %>%
summarize(total_01 = sum(ICD10_01), total_02 = sum(ICD10_02), total_03 = sum(ICD10_03),
total_04 = sum(ICD10_04), total_05 = sum(ICD10_05), total_06 = sum(ICD10_06),
total_07 = sum(ICD10_07), total_08 = sum(ICD10_08), total_09 = sum(ICD10_09),
total_10 = sum(ICD10_10), total_11 = sum(ICD10_11), total_12 = sum(ICD10_12),
total_13 = sum(ICD10_13), total_14 = sum(ICD10_14), total_15 = sum(ICD10_15),
total_16 = sum(ICD10_16)) %>%
gather(key = icd_code, value = total, total_01:total_16) %>%
separate(icd_code, into = c("total_char","icd_code"), sep = "_") %>%
select(-total_char) %>%
mutate(burden = total/sum(total)*100) %>%
mutate(disease = case_when(
icd_code == "01" ~ "Infectious diseases",
icd_code == "02" ~ "Neoplasms",
icd_code == "03" ~ "Blood diseases",
icd_code == "04" ~ "Nutritional/ metabolic diseases",
icd_code == "05" ~ "Mental/behavioral disorders",
icd_code == "06" ~ "Nervous system diseases",
icd_code == "07" ~ "Eye diseases",
icd_code == "08" ~ "Ear diseases",
icd_code == "09" ~ "Circulatory system diseases",
icd_code == "10" ~ "Respiratory system diseases",
icd_code == "11" ~ "Digestive system diseases",
icd_code == "12" ~ "Skin diseases",
icd_code == "13" ~ "Muscloskeletal system diseases",
icd_code == "14" ~ "Genitourinary system diseases",
icd_code == "15" ~ "Perinatal conditions",
icd_code == "16" ~ "Congenital malformations"
))
bar_graph %>%
ggplot(aes(x = reorder(disease, -burden), y = burden)) +
geom_bar(stat = "identity") +
labs(
title = "Proportion of people with specified diseases",
x = "Disease",
y = "Proportion (%)"
) +
viridis::scale_color_viridis(
name = "Location",
discrete = TRUE
) +
theme_bw() +
theme(axis.text.x = element_text(angle = 90, size = 7))
### Baseline 0
baseline_loneliness_0 = wave_1_data %>%
filter(MHUCLA_LONELINESS_1 == 0 & SEX == "(1) Male" & CS006 == "(6) Widowed") %>%
select(ID, SEX, CS006, MHUCLA_LONELINESS_1) %>%
merge(wave_2_data, by = "ID") %>%
merge(wave_3_data, by = "ID") %>%
select(ID, MHUCLA_LONELINESS_1, MHUCLA_LONELINESS_2, MHUCLA_LONELINESS_3) %>%
gather(key = wave, value = loneliness_value, MHUCLA_LONELINESS_1:MHUCLA_LONELINESS_3) %>%
mutate(wave = case_when(
wave == "MHUCLA_LONELINESS_1" ~ 1,
wave == "MHUCLA_LONELINESS_2" ~ 2,
wave == "MHUCLA_LONELINESS_3" ~ 3
)) %>%
filter(loneliness_value != "NA")
baseline_0 = baseline_loneliness_0 %>%
ggplot(aes(x = wave, y = loneliness_value)) +
geom_smooth(se = FALSE) +
ylim(0,10)
### Baseline 1
baseline_loneliness_1 = wave_1_data %>%
filter(MHUCLA_LONELINESS_1 == 1 & SEX == "(1) Male" & CS006 == "(6) Widowed") %>%
select(ID, SEX, CS006, MHUCLA_LONELINESS_1) %>%
merge(wave_2_data, by = "ID") %>%
merge(wave_3_data, by = "ID") %>%
select(ID, MHUCLA_LONELINESS_1, MHUCLA_LONELINESS_2, MHUCLA_LONELINESS_3) %>%
gather(key = wave, value = loneliness_value, MHUCLA_LONELINESS_1:MHUCLA_LONELINESS_3) %>%
mutate(wave = case_when(
wave == "MHUCLA_LONELINESS_1" ~ 1,
wave == "MHUCLA_LONELINESS_2" ~ 2,
wave == "MHUCLA_LONELINESS_3" ~ 3
)) %>%
filter(loneliness_value != "NA")
baseline_1 = baseline_loneliness_1 %>%
ggplot(aes(x = wave, y = loneliness_value)) +
geom_smooth(se = FALSE) +
ylim(0,10)
### Baseline 2
baseline_loneliness_2 = wave_1_data %>%
filter(MHUCLA_LONELINESS_1 == 2 & SEX == "(1) Male" & CS006 == "(6) Widowed") %>%
select(ID, SEX, CS006, MHUCLA_LONELINESS_1) %>%
merge(wave_2_data, by = "ID") %>%
merge(wave_3_data, by = "ID") %>%
select(ID, MHUCLA_LONELINESS_1, MHUCLA_LONELINESS_2, MHUCLA_LONELINESS_3) %>%
gather(key = wave, value = loneliness_value, MHUCLA_LONELINESS_1:MHUCLA_LONELINESS_3) %>%
mutate(wave = case_when(
wave == "MHUCLA_LONELINESS_1" ~ 1,
wave == "MHUCLA_LONELINESS_2" ~ 2,
wave == "MHUCLA_LONELINESS_3" ~ 3
)) %>%
filter(loneliness_value != "NA")
baseline_2 = baseline_loneliness_2 %>%
ggplot(aes(x = wave, y = loneliness_value)) +
geom_smooth(se = FALSE) +
ylim(0,10)
### Baseline 3
baseline_loneliness_3 = wave_1_data %>%
filter(MHUCLA_LONELINESS_1 == 3 & SEX == "(1) Male" & CS006 == "(6) Widowed") %>%
select(ID, SEX, CS006, MHUCLA_LONELINESS_1) %>%
merge(wave_2_data, by = "ID") %>%
merge(wave_3_data, by = "ID") %>%
select(ID, MHUCLA_LONELINESS_1, MHUCLA_LONELINESS_2, MHUCLA_LONELINESS_3) %>%
gather(key = wave, value = loneliness_value, MHUCLA_LONELINESS_1:MHUCLA_LONELINESS_3) %>%
mutate(wave = case_when(
wave == "MHUCLA_LONELINESS_1" ~ 1,
wave == "MHUCLA_LONELINESS_2" ~ 2,
wave == "MHUCLA_LONELINESS_3" ~ 3
)) %>%
filter(loneliness_value != "NA")
baseline_3 = baseline_loneliness_3 %>%
ggplot(aes(x = wave, y = loneliness_value)) +
geom_smooth(se = FALSE) +
ylim(0,10)
### Baseline 4
baseline_loneliness_4 = wave_1_data %>%
filter(MHUCLA_LONELINESS_1 == 4 & SEX == "(1) Male" & CS006 == "(6) Widowed") %>%
select(ID, SEX, CS006, MHUCLA_LONELINESS_1) %>%
merge(wave_2_data, by = "ID") %>%
merge(wave_3_data, by = "ID") %>%
select(ID, MHUCLA_LONELINESS_1, MHUCLA_LONELINESS_2, MHUCLA_LONELINESS_3) %>%
gather(key = wave, value = loneliness_value, MHUCLA_LONELINESS_1:MHUCLA_LONELINESS_3) %>%
mutate(wave = case_when(
wave == "MHUCLA_LONELINESS_1" ~ 1,
wave == "MHUCLA_LONELINESS_2" ~ 2,
wave == "MHUCLA_LONELINESS_3" ~ 3
)) %>%
filter(loneliness_value != "NA")
baseline_4 = baseline_loneliness_4 %>%
ggplot(aes(x = wave, y = loneliness_value)) +
geom_smooth(se = FALSE) +
ylim(0,10)
### Baseline 5
baseline_loneliness_5 = wave_1_data %>%
filter(MHUCLA_LONELINESS_1 == 5 & SEX == "(1) Male" & CS006 == "(6) Widowed") %>%
select(ID, SEX, CS006, MHUCLA_LONELINESS_1) %>%
merge(wave_2_data, by = "ID") %>%
merge(wave_3_data, by = "ID") %>%
select(ID, MHUCLA_LONELINESS_1, MHUCLA_LONELINESS_2, MHUCLA_LONELINESS_3) %>%
gather(key = wave, value = loneliness_value, MHUCLA_LONELINESS_1:MHUCLA_LONELINESS_3) %>%
mutate(wave = case_when(
wave == "MHUCLA_LONELINESS_1" ~ 1,
wave == "MHUCLA_LONELINESS_2" ~ 2,
wave == "MHUCLA_LONELINESS_3" ~ 3
)) %>%
filter(loneliness_value != "NA")
baseline_5 = baseline_loneliness_5 %>%
ggplot(aes(x = wave, y = loneliness_value)) +
geom_smooth(se = FALSE) +
ylim(0,10)
### Baseline 6
baseline_loneliness_6 = wave_1_data %>%
filter(MHUCLA_LONELINESS_1 == 6 & SEX == "(1) Male" & CS006 == "(6) Widowed") %>%
select(ID, SEX, CS006, MHUCLA_LONELINESS_1) %>%
merge(wave_2_data, by = "ID") %>%
merge(wave_3_data, by = "ID") %>%
select(ID, MHUCLA_LONELINESS_1, MHUCLA_LONELINESS_2, MHUCLA_LONELINESS_3) %>%
gather(key = wave, value = loneliness_value, MHUCLA_LONELINESS_1:MHUCLA_LONELINESS_3) %>%
mutate(wave = case_when(
wave == "MHUCLA_LONELINESS_1" ~ 1,
wave == "MHUCLA_LONELINESS_2" ~ 2,
wave == "MHUCLA_LONELINESS_3" ~ 3
)) %>%
filter(loneliness_value != "NA")
baseline_6 = baseline_loneliness_6 %>%
ggplot(aes(x = wave, y = loneliness_value)) +
geom_smooth(se = FALSE) +
ylim(0,10)
### Baseline 7
baseline_loneliness_7 = wave_1_data %>%
filter(MHUCLA_LONELINESS_1 == 7 & SEX == "(1) Male" & CS006 == "(6) Widowed") %>%
select(ID, SEX, CS006, MHUCLA_LONELINESS_1) %>%
merge(wave_2_data, by = "ID") %>%
merge(wave_3_data, by = "ID") %>%
select(ID, MHUCLA_LONELINESS_1, MHUCLA_LONELINESS_2, MHUCLA_LONELINESS_3) %>%
gather(key = wave, value = loneliness_value, MHUCLA_LONELINESS_1:MHUCLA_LONELINESS_3) %>%
mutate(wave = case_when(
wave == "MHUCLA_LONELINESS_1" ~ 1,
wave == "MHUCLA_LONELINESS_2" ~ 2,
wave == "MHUCLA_LONELINESS_3" ~ 3
)) %>%
filter(loneliness_value != "NA")
baseline_7 = baseline_loneliness_7 %>%
ggplot(aes(x = wave, y = loneliness_value)) +
geom_smooth(se = FALSE) +
ylim(0,10)
### Baseline 8
baseline_loneliness_8 = wave_1_data %>%
filter(MHUCLA_LONELINESS_1 == 8 & SEX == "(1) Male" & CS006 == "(6) Widowed") %>%
select(ID, SEX, CS006, MHUCLA_LONELINESS_1) %>%
merge(wave_2_data, by = "ID") %>%
merge(wave_3_data, by = "ID") %>%
select(ID, MHUCLA_LONELINESS_1, MHUCLA_LONELINESS_2, MHUCLA_LONELINESS_3) %>%
gather(key = wave, value = loneliness_value, MHUCLA_LONELINESS_1:MHUCLA_LONELINESS_3) %>%
mutate(wave = case_when(
wave == "MHUCLA_LONELINESS_1" ~ 1,
wave == "MHUCLA_LONELINESS_2" ~ 2,
wave == "MHUCLA_LONELINESS_3" ~ 3
)) %>%
filter(loneliness_value != "NA")
baseline_8 = baseline_loneliness_8 %>%
ggplot(aes(x = wave, y = loneliness_value)) +
geom_smooth(se = FALSE) +
ylim(0,10)
### Baseline 9
baseline_loneliness_9 = wave_1_data %>%
filter(MHUCLA_LONELINESS_1 == 9 & SEX == "(1) Male" & CS006 == "(6) Widowed") %>%
select(ID, SEX, CS006, MHUCLA_LONELINESS_1) %>%
merge(wave_2_data, by = "ID") %>%
merge(wave_3_data, by = "ID") %>%
select(ID, MHUCLA_LONELINESS_1, MHUCLA_LONELINESS_2, MHUCLA_LONELINESS_3) %>%
gather(key = wave, value = loneliness_value, MHUCLA_LONELINESS_1:MHUCLA_LONELINESS_3) %>%
mutate(wave = case_when(
wave == "MHUCLA_LONELINESS_1" ~ 1,
wave == "MHUCLA_LONELINESS_2" ~ 2,
wave == "MHUCLA_LONELINESS_3" ~ 3
)) %>%
filter(loneliness_value != "NA")
baseline_9 = baseline_loneliness_9 %>%
ggplot(aes(x = wave, y = loneliness_value)) +
geom_smooth(se = FALSE) +
ylim(0,10)
### Baseline 10
baseline_loneliness_10 = wave_1_data %>%
filter(MHUCLA_LONELINESS_1 == 10 & SEX == "(1) Male" & CS006 == "(6) Widowed") %>%
select(ID, SEX, CS006, MHUCLA_LONELINESS_1) %>%
merge(wave_2_data, by = "ID") %>%
merge(wave_3_data, by = "ID") %>%
select(ID, MHUCLA_LONELINESS_1, MHUCLA_LONELINESS_2, MHUCLA_LONELINESS_3) %>%
gather(key = wave, value = loneliness_value, MHUCLA_LONELINESS_1:MHUCLA_LONELINESS_3) %>%
mutate(wave = case_when(
wave == "MHUCLA_LONELINESS_1" ~ 1,
wave == "MHUCLA_LONELINESS_2" ~ 2,
wave == "MHUCLA_LONELINESS_3" ~ 3
)) %>%
filter(loneliness_value != "NA")
baseline_10 = baseline_loneliness_10 %>%
ggplot(aes(x = wave, y = loneliness_value)) +
geom_smooth(se = FALSE) +
ylim(0,10)
(baseline_0 + baseline_1 + baseline_2 + baseline_3 + baseline_4 + baseline_5 + baseline_6 + baseline_7 + baseline_8 + baseline_9)## `geom_smooth()` using method = 'loess' and formula 'y ~ x'## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.99## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 2.01## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 5.2454e-17## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 4.0401## `geom_smooth()` using method = 'loess' and formula 'y ~ x'## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.99## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 2.01## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 2.0661e-16## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 4.0401## `geom_smooth()` using method = 'loess' and formula 'y ~ x'## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.99## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 2.01## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 2.1055e-16## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 4.0401## `geom_smooth()` using method = 'loess' and formula 'y ~ x'## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.99## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 2.01## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 1.9961e-16## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 4.0401## `geom_smooth()` using method = 'loess' and formula 'y ~ x'## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.99## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 2.01## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 1.0184e-16## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 4.0401## `geom_smooth()` using method = 'loess' and formula 'y ~ x'## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.99## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 2.01## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 2.3477e-16## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 4.0401## `geom_smooth()` using method = 'loess' and formula 'y ~ x'## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.99## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 2.01## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 1.1404e-16## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 4.0401## `geom_smooth()` using method = 'loess' and formula 'y ~ x'## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.99## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 2.01## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 1.2425e-16## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 4.0401## `geom_smooth()` using method = 'loess' and formula 'y ~ x'## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.99## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 1.01## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 4.0401## `geom_smooth()` using method = 'loess' and formula 'y ~ x'## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : span too small. fewer data values than degrees of freedom.## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.99## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 1.01## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 4.0401